What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Segmentation, Computer vision, Object and Natural language processing.
She has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
Her research in Segmentation is mostly concerned with Image segmentation.
Her biological study spans a wide range of topics, including Bounding overwatch and Pattern recognition.
Her work in Object covers topics such as Parsing which are related to areas like Noun, Coreference and Spatial contextual awareness.
Her Natural language processing study integrates concerns from other disciplines, such as Ranking, Unsupervised learning and Ranking.
Her most cited work include:
- Scene Parsing through ADE20K Dataset (857 citations)
- Skip-Thought Vectors (834 citations)
- Skip-thought vectors (759 citations)
What are the main themes of her work throughout her whole career to date?
Sanja Fidler spends much of her time researching Artificial intelligence, Segmentation, Computer vision, Pattern recognition and Object.
Her Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing.
Her research in Natural language processing tackles topics such as Semantics which are related to areas like Visualization.
Her studies deal with areas such as Pixel, Image sensor, Deep learning and Convolutional neural network as well as Segmentation.
Her Pattern recognition research includes themes of Artificial neural network, Probabilistic logic, Generative model and Polygon.
The Object study combines topics in areas such as Annotation, Point cloud, Noise and Benchmark.
She most often published in these fields:
- Artificial intelligence (82.38%)
- Segmentation (27.75%)
- Computer vision (22.91%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (82.38%)
- Machine learning (19.82%)
- Object (19.38%)
In recent papers she was focusing on the following fields of study:
Sanja Fidler focuses on Artificial intelligence, Machine learning, Object, Image and Segmentation.
Her Artificial intelligence research is multidisciplinary, relying on both Pattern recognition, Computer vision and Graphics.
Her work on Image segmentation, Feature vector and Unsupervised learning as part of general Pattern recognition study is frequently linked to Structure, bridging the gap between disciplines.
Her study explores the link between Computer vision and topics such as Grid that cross with problems in Feature.
Sanja Fidler has included themes like Contextual image classification, Object detection, Personalization and Federated learning in her Machine learning study.
Her Object research is multidisciplinary, incorporating elements of Artificial neural network, Point cloud and Robotics.
Between 2019 and 2021, her most popular works were:
- The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines. (17 citations)
- Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D (12 citations)
- Learning to Evaluate Perception Models Using Planner-Centric Metrics (12 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Machine learning, Graphics, Image and Segmentation are her primary areas of study.
Her Artificial intelligence research includes elements of Task analysis and Computer vision.
Her Machine learning study incorporates themes from Object detection and Meta learning.
Her study on Graphics also encompasses disciplines like
- Rendering which is related to area like Level of detail, Interpolation and Octree,
- Consistency that intertwine with fields like Visualization and Generative model.
The study incorporates disciplines such as Object and Artificial neural network in addition to Image.
Her Object research is multidisciplinary, incorporating perspectives in Upsampling, Convolutional neural network and Feature.
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